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The compact genetic algorithm
Introduces the compact genetic algorithm (cGA) which represents the population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. Expand
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Finding Multimodal Solutions Using Restricted Tournament Selection
  • G. Harik
  • Computer Science
  • ICGA
  • 15 July 1995
This paper investigates a new technique for the solving of multimodal problems using genetic algorithms (GAs). Expand
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Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning Gene Linkage to E ciently Solve Problems of Bounded Di culty Using Genetic Algorithms by Georges Raif Harik Co-Chairs: Keki B. Irani and David E. Goldberg The complicated nature of modernExpand
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A parameter-less genetic algorithm
We propose a parameter-less genetic algorithm that relieves the user from having to set the parameters of the GA. Expand
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The gambler's ruin problem, genetic algorithms, and the sizing of populations
The paper presents a model for predicting the convergence quality of genetic algorithms that incorporates previous knowledge about decision making in genetic algorithms. Expand
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Learning Linkage
Blended learning is a powerful training strategy and approach for any organization seeking to optimize building employee skills and competencies with less time away from the job and lower travel costs. Expand
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Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA)
This chapter explores the relationship between the linkage-learning problem and that of learning probability distributions over multi-variate spaces. Expand
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Foundations of Genetic Algorithms
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The gambler''s ruin problem
Consider a gambler who starts with an initial fortune of $1 and then on each successive gamble either wins $1 or loses $1 independent of the past with probabilities p and q = 1−p respectively. Expand
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